Overview

Dataset statistics

Number of variables29
Number of observations2216
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory519.4 KiB
Average record size in memory240.0 B

Variable types

Numeric15
Categorical13
DateTime1

Alerts

Z_CostContact has constant value "3"Constant
Z_Revenue has constant value "11"Constant
AcceptedCmp3 is highly imbalanced (62.1%)Imbalance
AcceptedCmp4 is highly imbalanced (61.9%)Imbalance
AcceptedCmp5 is highly imbalanced (62.3%)Imbalance
AcceptedCmp1 is highly imbalanced (65.7%)Imbalance
AcceptedCmp2 is highly imbalanced (89.7%)Imbalance
Complain is highly imbalanced (92.3%)Imbalance
ID has unique valuesUnique
Recency has 28 (1.3%) zerosZeros
MntFruits has 395 (17.8%) zerosZeros
MntFishProducts has 379 (17.1%) zerosZeros
MntSweetProducts has 413 (18.6%) zerosZeros
MntGoldProds has 61 (2.8%) zerosZeros
NumDealsPurchases has 44 (2.0%) zerosZeros
NumWebPurchases has 48 (2.2%) zerosZeros
NumCatalogPurchases has 576 (26.0%) zerosZeros

Reproduction

Analysis started2024-07-20 19:51:50.563938
Analysis finished2024-07-20 19:52:12.549772
Duration21.99 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

ID
Real number (ℝ)

UNIQUE 

Distinct2216
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5588.3533
Minimum0
Maximum11191
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2024-07-20T16:52:12.688256image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile572.75
Q12814.75
median5458.5
Q38421.75
95-th percentile10676.5
Maximum11191
Range11191
Interquartile range (IQR)5607

Descriptive statistics

Standard deviation3249.3763
Coefficient of variation (CV)0.58145505
Kurtosis-1.1896767
Mean5588.3533
Median Absolute Deviation (MAD)2791
Skewness0.040459216
Sum12383791
Variance10558446
MonotonicityNot monotonic
2024-07-20T16:52:12.850995image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5524 1
 
< 0.1%
6885 1
 
< 0.1%
3478 1
 
< 0.1%
7494 1
 
< 0.1%
1763 1
 
< 0.1%
7250 1
 
< 0.1%
2005 1
 
< 0.1%
10770 1
 
< 0.1%
2072 1
 
< 0.1%
3643 1
 
< 0.1%
Other values (2206) 2206
99.5%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
9 1
< 0.1%
13 1
< 0.1%
17 1
< 0.1%
20 1
< 0.1%
22 1
< 0.1%
24 1
< 0.1%
25 1
< 0.1%
35 1
< 0.1%
ValueCountFrequency (%)
11191 1
< 0.1%
11188 1
< 0.1%
11187 1
< 0.1%
11181 1
< 0.1%
11178 1
< 0.1%
11176 1
< 0.1%
11171 1
< 0.1%
11166 1
< 0.1%
11148 1
< 0.1%
11133 1
< 0.1%

Year_Birth
Real number (ℝ)

Distinct59
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1968.8204
Minimum1893
Maximum1996
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2024-07-20T16:52:13.008131image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1893
5-th percentile1950
Q11959
median1970
Q31977
95-th percentile1988
Maximum1996
Range103
Interquartile range (IQR)18

Descriptive statistics

Standard deviation11.985554
Coefficient of variation (CV)0.0060876828
Kurtosis0.73467044
Mean1968.8204
Median Absolute Deviation (MAD)9
Skewness-0.35366147
Sum4362906
Variance143.65351
MonotonicityNot monotonic
2024-07-20T16:52:13.226406image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1976 89
 
4.0%
1971 86
 
3.9%
1975 83
 
3.7%
1972 78
 
3.5%
1978 76
 
3.4%
1970 75
 
3.4%
1965 74
 
3.3%
1973 72
 
3.2%
1969 70
 
3.2%
1974 69
 
3.1%
Other values (49) 1444
65.2%
ValueCountFrequency (%)
1893 1
 
< 0.1%
1899 1
 
< 0.1%
1900 1
 
< 0.1%
1940 1
 
< 0.1%
1941 1
 
< 0.1%
1943 6
 
0.3%
1944 7
0.3%
1945 8
0.4%
1946 16
0.7%
1947 16
0.7%
ValueCountFrequency (%)
1996 2
 
0.1%
1995 5
 
0.2%
1994 3
 
0.1%
1993 5
 
0.2%
1992 13
0.6%
1991 15
0.7%
1990 18
0.8%
1989 29
1.3%
1988 29
1.3%
1987 27
1.2%

Education
Categorical

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size34.6 KiB
Graduation
1116 
PhD
481 
Master
365 
2n Cycle
200 
Basic
 
54

Length

Max length10
Median length10
Mean length7.5194043
Min length3

Characters and Unicode

Total characters16663
Distinct characters22
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGraduation
2nd rowGraduation
3rd rowGraduation
4th rowGraduation
5th rowPhD

Common Values

ValueCountFrequency (%)
Graduation 1116
50.4%
PhD 481
21.7%
Master 365
 
16.5%
2n Cycle 200
 
9.0%
Basic 54
 
2.4%

Length

2024-07-20T16:52:13.367826image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-20T16:52:13.494678image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
graduation 1116
46.2%
phd 481
19.9%
master 365
 
15.1%
2n 200
 
8.3%
cycle 200
 
8.3%
basic 54
 
2.2%

Most occurring characters

ValueCountFrequency (%)
a 2651
15.9%
r 1481
8.9%
t 1481
8.9%
n 1316
 
7.9%
i 1170
 
7.0%
G 1116
 
6.7%
d 1116
 
6.7%
u 1116
 
6.7%
o 1116
 
6.7%
e 565
 
3.4%
Other values (12) 3535
21.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13566
81.4%
Uppercase Letter 2697
 
16.2%
Decimal Number 200
 
1.2%
Space Separator 200
 
1.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2651
19.5%
r 1481
10.9%
t 1481
10.9%
n 1316
9.7%
i 1170
8.6%
d 1116
8.2%
u 1116
8.2%
o 1116
8.2%
e 565
 
4.2%
h 481
 
3.5%
Other values (4) 1073
7.9%
Uppercase Letter
ValueCountFrequency (%)
G 1116
41.4%
D 481
17.8%
P 481
17.8%
M 365
 
13.5%
C 200
 
7.4%
B 54
 
2.0%
Decimal Number
ValueCountFrequency (%)
2 200
100.0%
Space Separator
ValueCountFrequency (%)
200
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 16263
97.6%
Common 400
 
2.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2651
16.3%
r 1481
9.1%
t 1481
9.1%
n 1316
8.1%
i 1170
 
7.2%
G 1116
 
6.9%
d 1116
 
6.9%
u 1116
 
6.9%
o 1116
 
6.9%
e 565
 
3.5%
Other values (10) 3135
19.3%
Common
ValueCountFrequency (%)
2 200
50.0%
200
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16663
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 2651
15.9%
r 1481
8.9%
t 1481
8.9%
n 1316
 
7.9%
i 1170
 
7.0%
G 1116
 
6.7%
d 1116
 
6.7%
u 1116
 
6.7%
o 1116
 
6.7%
e 565
 
3.4%
Other values (12) 3535
21.2%

Marital_Status
Categorical

Distinct8
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size34.6 KiB
Married
857 
Together
573 
Single
471 
Divorced
232 
Widow
 
76
Other values (3)
 
7

Length

Max length8
Median length7
Mean length7.0758123
Min length4

Characters and Unicode

Total characters15680
Distinct characters26
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSingle
2nd rowSingle
3rd rowTogether
4th rowTogether
5th rowMarried

Common Values

ValueCountFrequency (%)
Married 857
38.7%
Together 573
25.9%
Single 471
21.3%
Divorced 232
 
10.5%
Widow 76
 
3.4%
Alone 3
 
0.1%
Absurd 2
 
0.1%
YOLO 2
 
0.1%

Length

2024-07-20T16:52:13.657821image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-20T16:52:13.838747image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
married 857
38.7%
together 573
25.9%
single 471
21.3%
divorced 232
 
10.5%
widow 76
 
3.4%
alone 3
 
0.1%
absurd 2
 
0.1%
yolo 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e 2709
17.3%
r 2521
16.1%
i 1636
10.4%
d 1167
7.4%
g 1044
 
6.7%
o 884
 
5.6%
M 857
 
5.5%
a 857
 
5.5%
T 573
 
3.7%
t 573
 
3.7%
Other values (16) 2859
18.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13458
85.8%
Uppercase Letter 2222
 
14.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 2709
20.1%
r 2521
18.7%
i 1636
12.2%
d 1167
8.7%
g 1044
 
7.8%
o 884
 
6.6%
a 857
 
6.4%
t 573
 
4.3%
h 573
 
4.3%
n 474
 
3.5%
Other values (7) 1020
 
7.6%
Uppercase Letter
ValueCountFrequency (%)
M 857
38.6%
T 573
25.8%
S 471
21.2%
D 232
 
10.4%
W 76
 
3.4%
A 5
 
0.2%
O 4
 
0.2%
Y 2
 
0.1%
L 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 15680
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 2709
17.3%
r 2521
16.1%
i 1636
10.4%
d 1167
7.4%
g 1044
 
6.7%
o 884
 
5.6%
M 857
 
5.5%
a 857
 
5.5%
T 573
 
3.7%
t 573
 
3.7%
Other values (16) 2859
18.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15680
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 2709
17.3%
r 2521
16.1%
i 1636
10.4%
d 1167
7.4%
g 1044
 
6.7%
o 884
 
5.6%
M 857
 
5.5%
a 857
 
5.5%
T 573
 
3.7%
t 573
 
3.7%
Other values (16) 2859
18.2%

Income
Real number (ℝ)

Distinct1974
Distinct (%)89.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52247.251
Minimum1730
Maximum666666
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2024-07-20T16:52:14.001312image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1730
5-th percentile18985.5
Q135303
median51381.5
Q368522
95-th percentile84130
Maximum666666
Range664936
Interquartile range (IQR)33219

Descriptive statistics

Standard deviation25173.077
Coefficient of variation (CV)0.48180672
Kurtosis159.6367
Mean52247.251
Median Absolute Deviation (MAD)16557.5
Skewness6.7634874
Sum1.1577991 × 108
Variance6.3368379 × 108
MonotonicityNot monotonic
2024-07-20T16:52:14.459116image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7500 12
 
0.5%
35860 4
 
0.2%
37760 3
 
0.1%
83844 3
 
0.1%
63841 3
 
0.1%
18929 3
 
0.1%
47025 3
 
0.1%
34176 3
 
0.1%
48432 3
 
0.1%
39922 3
 
0.1%
Other values (1964) 2176
98.2%
ValueCountFrequency (%)
1730 1
< 0.1%
2447 1
< 0.1%
3502 1
< 0.1%
4023 1
< 0.1%
4428 1
< 0.1%
4861 1
< 0.1%
5305 1
< 0.1%
5648 1
< 0.1%
6560 1
< 0.1%
6835 1
< 0.1%
ValueCountFrequency (%)
666666 1
< 0.1%
162397 1
< 0.1%
160803 1
< 0.1%
157733 1
< 0.1%
157243 1
< 0.1%
157146 1
< 0.1%
156924 1
< 0.1%
153924 1
< 0.1%
113734 1
< 0.1%
105471 1
< 0.1%

Kidhome
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size34.6 KiB
0
1283 
1
887 
2
 
46

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2216
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 1283
57.9%
1 887
40.0%
2 46
 
2.1%

Length

2024-07-20T16:52:14.597808image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-20T16:52:14.718779image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 1283
57.9%
1 887
40.0%
2 46
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 1283
57.9%
1 887
40.0%
2 46
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2216
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1283
57.9%
1 887
40.0%
2 46
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 2216
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1283
57.9%
1 887
40.0%
2 46
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2216
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1283
57.9%
1 887
40.0%
2 46
 
2.1%

Teenhome
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size34.6 KiB
0
1147 
1
1018 
2
 
51

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2216
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1147
51.8%
1 1018
45.9%
2 51
 
2.3%

Length

2024-07-20T16:52:14.833497image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-20T16:52:14.961247image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 1147
51.8%
1 1018
45.9%
2 51
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 1147
51.8%
1 1018
45.9%
2 51
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2216
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1147
51.8%
1 1018
45.9%
2 51
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Common 2216
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1147
51.8%
1 1018
45.9%
2 51
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2216
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1147
51.8%
1 1018
45.9%
2 51
 
2.3%
Distinct662
Distinct (%)29.9%
Missing0
Missing (%)0.0%
Memory size34.6 KiB
Minimum2012-07-30 00:00:00
Maximum2014-06-29 00:00:00
2024-07-20T16:52:15.111556image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:15.272538image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Recency
Real number (ℝ)

ZEROS 

Distinct100
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.012635
Minimum0
Maximum99
Zeros28
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2024-07-20T16:52:15.420917image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q124
median49
Q374
95-th percentile94
Maximum99
Range99
Interquartile range (IQR)50

Descriptive statistics

Standard deviation28.948352
Coefficient of variation (CV)0.59063038
Kurtosis-1.1997769
Mean49.012635
Median Absolute Deviation (MAD)25
Skewness0.0016477067
Sum108612
Variance838.00706
MonotonicityNot monotonic
2024-07-20T16:52:15.580721image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56 37
 
1.7%
30 32
 
1.4%
54 32
 
1.4%
46 31
 
1.4%
92 30
 
1.4%
65 30
 
1.4%
71 29
 
1.3%
3 29
 
1.3%
29 29
 
1.3%
49 29
 
1.3%
Other values (90) 1908
86.1%
ValueCountFrequency (%)
0 28
1.3%
1 24
1.1%
2 28
1.3%
3 29
1.3%
4 26
1.2%
5 15
0.7%
6 21
0.9%
7 12
0.5%
8 25
1.1%
9 24
1.1%
ValueCountFrequency (%)
99 17
0.8%
98 22
1.0%
97 20
0.9%
96 23
1.0%
95 18
0.8%
94 26
1.2%
93 21
0.9%
92 30
1.4%
91 18
0.8%
90 20
0.9%

MntWines
Real number (ℝ)

Distinct776
Distinct (%)35.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean305.09161
Minimum0
Maximum1493
Zeros13
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2024-07-20T16:52:15.720620image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q124
median174.5
Q3505
95-th percentile1000.25
Maximum1493
Range1493
Interquartile range (IQR)481

Descriptive statistics

Standard deviation337.32792
Coefficient of variation (CV)1.1056611
Kurtosis0.58274112
Mean305.09161
Median Absolute Deviation (MAD)165.5
Skewness1.1707201
Sum676083
Variance113790.13
MonotonicityNot monotonic
2024-07-20T16:52:15.881074image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 42
 
1.9%
1 37
 
1.7%
6 37
 
1.7%
5 37
 
1.7%
4 33
 
1.5%
8 30
 
1.4%
3 30
 
1.4%
9 28
 
1.3%
12 25
 
1.1%
14 24
 
1.1%
Other values (766) 1893
85.4%
ValueCountFrequency (%)
0 13
 
0.6%
1 37
1.7%
2 42
1.9%
3 30
1.4%
4 33
1.5%
5 37
1.7%
6 37
1.7%
7 21
0.9%
8 30
1.4%
9 28
1.3%
ValueCountFrequency (%)
1493 1
< 0.1%
1492 2
0.1%
1486 1
< 0.1%
1478 2
0.1%
1462 1
< 0.1%
1459 1
< 0.1%
1449 1
< 0.1%
1396 1
< 0.1%
1394 1
< 0.1%
1379 1
< 0.1%

MntFruits
Real number (ℝ)

ZEROS 

Distinct158
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.356047
Minimum0
Maximum199
Zeros395
Zeros (%)17.8%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2024-07-20T16:52:16.027547image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median8
Q333
95-th percentile122.25
Maximum199
Range199
Interquartile range (IQR)31

Descriptive statistics

Standard deviation39.793917
Coefficient of variation (CV)1.5098591
Kurtosis4.0540815
Mean26.356047
Median Absolute Deviation (MAD)8
Skewness2.1016575
Sum58405
Variance1583.5558
MonotonicityNot monotonic
2024-07-20T16:52:16.188776image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 395
 
17.8%
1 158
 
7.1%
2 119
 
5.4%
3 114
 
5.1%
4 103
 
4.6%
7 67
 
3.0%
5 62
 
2.8%
6 62
 
2.8%
12 50
 
2.3%
8 48
 
2.2%
Other values (148) 1038
46.8%
ValueCountFrequency (%)
0 395
17.8%
1 158
 
7.1%
2 119
 
5.4%
3 114
 
5.1%
4 103
 
4.6%
5 62
 
2.8%
6 62
 
2.8%
7 67
 
3.0%
8 48
 
2.2%
9 35
 
1.6%
ValueCountFrequency (%)
199 2
0.1%
197 1
 
< 0.1%
194 3
0.1%
193 2
0.1%
190 1
 
< 0.1%
189 1
 
< 0.1%
185 2
0.1%
184 1
 
< 0.1%
183 3
0.1%
181 1
 
< 0.1%

MntMeatProducts
Real number (ℝ)

Distinct554
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean166.99594
Minimum0
Maximum1725
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2024-07-20T16:52:16.329495image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q116
median68
Q3232.25
95-th percentile687.5
Maximum1725
Range1725
Interquartile range (IQR)216.25

Descriptive statistics

Standard deviation224.28327
Coefficient of variation (CV)1.3430463
Kurtosis5.0554767
Mean166.99594
Median Absolute Deviation (MAD)60
Skewness2.0255768
Sum370063
Variance50302.986
MonotonicityNot monotonic
2024-07-20T16:52:16.489956image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 53
 
2.4%
5 50
 
2.3%
11 49
 
2.2%
8 45
 
2.0%
6 42
 
1.9%
10 40
 
1.8%
3 39
 
1.8%
9 37
 
1.7%
16 35
 
1.6%
12 34
 
1.5%
Other values (544) 1792
80.9%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 14
 
0.6%
2 30
1.4%
3 39
1.8%
4 30
1.4%
5 50
2.3%
6 42
1.9%
7 53
2.4%
8 45
2.0%
9 37
1.7%
ValueCountFrequency (%)
1725 2
0.1%
1622 1
< 0.1%
1582 1
< 0.1%
984 1
< 0.1%
981 1
< 0.1%
974 1
< 0.1%
968 1
< 0.1%
961 1
< 0.1%
951 2
0.1%
946 1
< 0.1%

MntFishProducts
Real number (ℝ)

ZEROS 

Distinct182
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.637635
Minimum0
Maximum259
Zeros379
Zeros (%)17.1%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2024-07-20T16:52:16.633646image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median12
Q350
95-th percentile169
Maximum259
Range259
Interquartile range (IQR)47

Descriptive statistics

Standard deviation54.752082
Coefficient of variation (CV)1.4547163
Kurtosis3.0764763
Mean37.637635
Median Absolute Deviation (MAD)12
Skewness1.916369
Sum83405
Variance2997.7905
MonotonicityNot monotonic
2024-07-20T16:52:16.785559image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 379
 
17.1%
2 152
 
6.9%
3 128
 
5.8%
4 108
 
4.9%
6 81
 
3.7%
7 66
 
3.0%
8 58
 
2.6%
10 54
 
2.4%
13 48
 
2.2%
11 46
 
2.1%
Other values (172) 1096
49.5%
ValueCountFrequency (%)
0 379
17.1%
1 10
 
0.5%
2 152
6.9%
3 128
 
5.8%
4 108
 
4.9%
5 1
 
< 0.1%
6 81
 
3.7%
7 66
 
3.0%
8 58
 
2.6%
10 54
 
2.4%
ValueCountFrequency (%)
259 1
 
< 0.1%
258 3
0.1%
254 1
 
< 0.1%
253 1
 
< 0.1%
250 3
0.1%
247 1
 
< 0.1%
246 1
 
< 0.1%
242 1
 
< 0.1%
240 2
0.1%
237 2
0.1%

MntSweetProducts
Real number (ℝ)

ZEROS 

Distinct176
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.028881
Minimum0
Maximum262
Zeros413
Zeros (%)18.6%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2024-07-20T16:52:16.922653image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median8
Q333
95-th percentile125.25
Maximum262
Range262
Interquartile range (IQR)32

Descriptive statistics

Standard deviation41.072046
Coefficient of variation (CV)1.5195615
Kurtosis4.1061406
Mean27.028881
Median Absolute Deviation (MAD)8
Skewness2.1033276
Sum59896
Variance1686.9129
MonotonicityNot monotonic
2024-07-20T16:52:17.073832image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 413
 
18.6%
1 161
 
7.3%
2 123
 
5.6%
3 101
 
4.6%
4 80
 
3.6%
5 65
 
2.9%
6 63
 
2.8%
7 57
 
2.6%
8 56
 
2.5%
12 45
 
2.0%
Other values (166) 1052
47.5%
ValueCountFrequency (%)
0 413
18.6%
1 161
 
7.3%
2 123
 
5.6%
3 101
 
4.6%
4 80
 
3.6%
5 65
 
2.9%
6 63
 
2.8%
7 57
 
2.6%
8 56
 
2.5%
9 42
 
1.9%
ValueCountFrequency (%)
262 1
 
< 0.1%
198 1
 
< 0.1%
197 1
 
< 0.1%
196 1
 
< 0.1%
195 1
 
< 0.1%
194 3
0.1%
192 3
0.1%
191 1
 
< 0.1%
189 2
0.1%
188 1
 
< 0.1%

MntGoldProds
Real number (ℝ)

ZEROS 

Distinct212
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.965253
Minimum0
Maximum321
Zeros61
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2024-07-20T16:52:17.209526image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q19
median24.5
Q356
95-th percentile165.25
Maximum321
Range321
Interquartile range (IQR)47

Descriptive statistics

Standard deviation51.815414
Coefficient of variation (CV)1.1785538
Kurtosis3.1563419
Mean43.965253
Median Absolute Deviation (MAD)18.5
Skewness1.8392309
Sum97427
Variance2684.8372
MonotonicityNot monotonic
2024-07-20T16:52:17.360684image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 71
 
3.2%
4 69
 
3.1%
3 68
 
3.1%
12 63
 
2.8%
5 63
 
2.8%
2 62
 
2.8%
0 61
 
2.8%
6 55
 
2.5%
7 52
 
2.3%
10 49
 
2.2%
Other values (202) 1603
72.3%
ValueCountFrequency (%)
0 61
2.8%
1 71
3.2%
2 62
2.8%
3 68
3.1%
4 69
3.1%
5 63
2.8%
6 55
2.5%
7 52
2.3%
8 40
1.8%
9 43
1.9%
ValueCountFrequency (%)
321 1
 
< 0.1%
291 1
 
< 0.1%
262 1
 
< 0.1%
249 1
 
< 0.1%
248 1
 
< 0.1%
247 1
 
< 0.1%
246 1
 
< 0.1%
245 1
 
< 0.1%
242 2
 
0.1%
241 6
0.3%

NumDealsPurchases
Real number (ℝ)

ZEROS 

Distinct15
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.323556
Minimum0
Maximum15
Zeros44
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2024-07-20T16:52:17.476272image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33
95-th percentile6
Maximum15
Range15
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9237156
Coefficient of variation (CV)0.82791879
Kurtosis8.9744901
Mean2.323556
Median Absolute Deviation (MAD)1
Skewness2.4152718
Sum5149
Variance3.7006819
MonotonicityNot monotonic
2024-07-20T16:52:17.599666image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 960
43.3%
2 493
22.2%
3 293
 
13.2%
4 188
 
8.5%
5 94
 
4.2%
6 60
 
2.7%
0 44
 
2.0%
7 39
 
1.8%
8 14
 
0.6%
9 8
 
0.4%
Other values (5) 23
 
1.0%
ValueCountFrequency (%)
0 44
 
2.0%
1 960
43.3%
2 493
22.2%
3 293
 
13.2%
4 188
 
8.5%
5 94
 
4.2%
6 60
 
2.7%
7 39
 
1.8%
8 14
 
0.6%
9 8
 
0.4%
ValueCountFrequency (%)
15 7
 
0.3%
13 3
 
0.1%
12 3
 
0.1%
11 5
 
0.2%
10 5
 
0.2%
9 8
 
0.4%
8 14
 
0.6%
7 39
1.8%
6 60
2.7%
5 94
4.2%

NumWebPurchases
Real number (ℝ)

ZEROS 

Distinct15
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0852888
Minimum0
Maximum27
Zeros48
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2024-07-20T16:52:17.711903image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q36
95-th percentile9
Maximum27
Range27
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.7409511
Coefficient of variation (CV)0.67093202
Kurtosis4.0721368
Mean4.0852888
Median Absolute Deviation (MAD)2
Skewness1.197037
Sum9053
Variance7.5128128
MonotonicityNot monotonic
2024-07-20T16:52:17.838190image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2 368
16.6%
1 348
15.7%
3 334
15.1%
4 277
12.5%
5 219
9.9%
6 201
9.1%
7 154
6.9%
8 102
 
4.6%
9 75
 
3.4%
0 48
 
2.2%
Other values (5) 90
 
4.1%
ValueCountFrequency (%)
0 48
 
2.2%
1 348
15.7%
2 368
16.6%
3 334
15.1%
4 277
12.5%
5 219
9.9%
6 201
9.1%
7 154
6.9%
8 102
 
4.6%
9 75
 
3.4%
ValueCountFrequency (%)
27 1
 
< 0.1%
25 1
 
< 0.1%
23 1
 
< 0.1%
11 44
 
2.0%
10 43
 
1.9%
9 75
 
3.4%
8 102
4.6%
7 154
6.9%
6 201
9.1%
5 219
9.9%

NumCatalogPurchases
Real number (ℝ)

ZEROS 

Distinct14
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6710289
Minimum0
Maximum28
Zeros576
Zeros (%)26.0%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2024-07-20T16:52:17.940665image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34
95-th percentile9
Maximum28
Range28
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.9267336
Coefficient of variation (CV)1.0957327
Kurtosis8.0671262
Mean2.6710289
Median Absolute Deviation (MAD)2
Skewness1.8810751
Sum5919
Variance8.5657698
MonotonicityNot monotonic
2024-07-20T16:52:18.056548image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 576
26.0%
1 492
22.2%
2 274
12.4%
3 182
 
8.2%
4 181
 
8.2%
5 137
 
6.2%
6 128
 
5.8%
7 79
 
3.6%
8 55
 
2.5%
10 47
 
2.1%
Other values (4) 65
 
2.9%
ValueCountFrequency (%)
0 576
26.0%
1 492
22.2%
2 274
12.4%
3 182
 
8.2%
4 181
 
8.2%
5 137
 
6.2%
6 128
 
5.8%
7 79
 
3.6%
8 55
 
2.5%
9 42
 
1.9%
ValueCountFrequency (%)
28 3
 
0.1%
22 1
 
< 0.1%
11 19
 
0.9%
10 47
 
2.1%
9 42
 
1.9%
8 55
 
2.5%
7 79
3.6%
6 128
5.8%
5 137
6.2%
4 181
8.2%

NumStorePurchases
Real number (ℝ)

Distinct14
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8009928
Minimum0
Maximum13
Zeros14
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2024-07-20T16:52:18.164723image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median5
Q38
95-th percentile12
Maximum13
Range13
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.2507848
Coefficient of variation (CV)0.56038422
Kurtosis-0.62646219
Mean5.8009928
Median Absolute Deviation (MAD)2
Skewness0.7018263
Sum12855
Variance10.567602
MonotonicityNot monotonic
2024-07-20T16:52:18.297913image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
3 484
21.8%
4 319
14.4%
2 220
9.9%
5 211
9.5%
6 177
 
8.0%
8 147
 
6.6%
7 141
 
6.4%
10 124
 
5.6%
9 106
 
4.8%
12 104
 
4.7%
Other values (4) 183
 
8.3%
ValueCountFrequency (%)
0 14
 
0.6%
1 6
 
0.3%
2 220
9.9%
3 484
21.8%
4 319
14.4%
5 211
9.5%
6 177
 
8.0%
7 141
 
6.4%
8 147
 
6.6%
9 106
 
4.8%
ValueCountFrequency (%)
13 83
 
3.7%
12 104
 
4.7%
11 80
 
3.6%
10 124
 
5.6%
9 106
 
4.8%
8 147
6.6%
7 141
6.4%
6 177
8.0%
5 211
9.5%
4 319
14.4%

NumWebVisitsMonth
Real number (ℝ)

Distinct16
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3190433
Minimum0
Maximum20
Zeros10
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2024-07-20T16:52:18.416688image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median6
Q37
95-th percentile8
Maximum20
Range20
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.4253585
Coefficient of variation (CV)0.45597646
Kurtosis1.8525767
Mean5.3190433
Median Absolute Deviation (MAD)2
Skewness0.21804305
Sum11787
Variance5.8823641
MonotonicityNot monotonic
2024-07-20T16:52:18.557097image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
7 387
17.5%
8 340
15.3%
6 335
15.1%
5 279
12.6%
4 217
9.8%
3 203
9.2%
2 201
9.1%
1 150
 
6.8%
9 82
 
3.7%
0 10
 
0.5%
Other values (6) 12
 
0.5%
ValueCountFrequency (%)
0 10
 
0.5%
1 150
 
6.8%
2 201
9.1%
3 203
9.2%
4 217
9.8%
5 279
12.6%
6 335
15.1%
7 387
17.5%
8 340
15.3%
9 82
 
3.7%
ValueCountFrequency (%)
20 3
 
0.1%
19 2
 
0.1%
17 1
 
< 0.1%
14 2
 
0.1%
13 1
 
< 0.1%
10 3
 
0.1%
9 82
 
3.7%
8 340
15.3%
7 387
17.5%
6 335
15.1%

AcceptedCmp3
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size34.6 KiB
0
2053 
1
 
163

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2216
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2053
92.6%
1 163
 
7.4%

Length

2024-07-20T16:52:18.680983image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-20T16:52:18.781689image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 2053
92.6%
1 163
 
7.4%

Most occurring characters

ValueCountFrequency (%)
0 2053
92.6%
1 163
 
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2216
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2053
92.6%
1 163
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
Common 2216
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2053
92.6%
1 163
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2216
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2053
92.6%
1 163
 
7.4%

AcceptedCmp4
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size34.6 KiB
0
2052 
1
 
164

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2216
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2052
92.6%
1 164
 
7.4%

Length

2024-07-20T16:52:18.891245image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-20T16:52:18.995095image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 2052
92.6%
1 164
 
7.4%

Most occurring characters

ValueCountFrequency (%)
0 2052
92.6%
1 164
 
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2216
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2052
92.6%
1 164
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
Common 2216
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2052
92.6%
1 164
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2216
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2052
92.6%
1 164
 
7.4%

AcceptedCmp5
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size34.6 KiB
0
2054 
1
 
162

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2216
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2054
92.7%
1 162
 
7.3%

Length

2024-07-20T16:52:19.105641image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-20T16:52:19.214569image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 2054
92.7%
1 162
 
7.3%

Most occurring characters

ValueCountFrequency (%)
0 2054
92.7%
1 162
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2216
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2054
92.7%
1 162
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
Common 2216
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2054
92.7%
1 162
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2216
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2054
92.7%
1 162
 
7.3%

AcceptedCmp1
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size34.6 KiB
0
2074 
1
 
142

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2216
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2074
93.6%
1 142
 
6.4%

Length

2024-07-20T16:52:19.333208image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-20T16:52:19.439416image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 2074
93.6%
1 142
 
6.4%

Most occurring characters

ValueCountFrequency (%)
0 2074
93.6%
1 142
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2216
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2074
93.6%
1 142
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
Common 2216
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2074
93.6%
1 142
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2216
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2074
93.6%
1 142
 
6.4%

AcceptedCmp2
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size34.6 KiB
0
2186 
1
 
30

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2216
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2186
98.6%
1 30
 
1.4%

Length

2024-07-20T16:52:19.554420image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-20T16:52:19.654304image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 2186
98.6%
1 30
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0 2186
98.6%
1 30
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2216
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2186
98.6%
1 30
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Common 2216
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2186
98.6%
1 30
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2216
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2186
98.6%
1 30
 
1.4%

Complain
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size34.6 KiB
0
2195 
1
 
21

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2216
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2195
99.1%
1 21
 
0.9%

Length

2024-07-20T16:52:19.762744image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-20T16:52:19.863725image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 2195
99.1%
1 21
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 2195
99.1%
1 21
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2216
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2195
99.1%
1 21
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Common 2216
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2195
99.1%
1 21
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2216
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2195
99.1%
1 21
 
0.9%

Z_CostContact
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size34.6 KiB
3
2216 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2216
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 2216
100.0%

Length

2024-07-20T16:52:19.971098image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-20T16:52:20.065490image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
3 2216
100.0%

Most occurring characters

ValueCountFrequency (%)
3 2216
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2216
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 2216
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2216
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 2216
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2216
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 2216
100.0%

Z_Revenue
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size34.6 KiB
11
2216 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters4432
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row11
2nd row11
3rd row11
4th row11
5th row11

Common Values

ValueCountFrequency (%)
11 2216
100.0%

Length

2024-07-20T16:52:20.171457image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-20T16:52:20.266808image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
11 2216
100.0%

Most occurring characters

ValueCountFrequency (%)
1 4432
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4432
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4432
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4432
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4432
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4432
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4432
100.0%

Response
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size34.6 KiB
0
1883 
1
333 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2216
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1883
85.0%
1 333
 
15.0%

Length

2024-07-20T16:52:20.371599image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-20T16:52:20.473757image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 1883
85.0%
1 333
 
15.0%

Most occurring characters

ValueCountFrequency (%)
0 1883
85.0%
1 333
 
15.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2216
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1883
85.0%
1 333
 
15.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2216
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1883
85.0%
1 333
 
15.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2216
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1883
85.0%
1 333
 
15.0%

Interactions

2024-07-20T16:52:10.733108image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:51.363633image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:52.767863image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:54.073021image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:55.486761image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:56.745218image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:58.216149image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:59.564747image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:00.890745image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:02.379310image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:03.730759image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:05.109465image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:06.547986image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:08.041667image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:09.393210image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:10.816674image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:51.455383image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:52.849077image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:54.155800image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:55.566547image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:56.829991image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:58.304912image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:59.650517image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:00.972526image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:02.467246image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:03.822515image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:05.232277image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:06.629654image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:08.121852image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:09.477159image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:10.906136image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:51.540155image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:52.932153image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:54.241598image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:55.647331image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:56.914234image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:58.391894image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:59.737286image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:01.054718image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:02.552018image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:03.907287image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:05.332090image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:06.711787image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:08.207259image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:09.575896image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:10.994021image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:51.626433image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:53.018111image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:54.329672image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:55.731495image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:57.001999image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:58.481654image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:59.824053image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:01.139492image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:02.635795image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:03.993057image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:05.426218image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:06.796531image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:08.294178image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:09.681054image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:11.077287image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:51.709211image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:53.098925image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:54.414809image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:55.810664image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:57.087132image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:58.567555image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:59.909825image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:01.240312image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:02.719571image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:04.079114image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:05.520589image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:06.877831image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:08.379649image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:09.780474image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:11.170149image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:51.805953image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:53.187314image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:54.504569image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:55.898430image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:57.183992image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:58.657315image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:59.999584image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:01.327080image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:02.817560image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:04.175280image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:05.613361image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:06.965404image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:08.470421image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:09.870234image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:11.266968image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:51.898704image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:53.278716image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:54.600313image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:55.986574image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:57.279968image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:58.750460image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:00.093464image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:01.414263image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:02.912387image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:04.269248image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:05.719661image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:07.057454image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:08.563329image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:09.962986image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:11.361092image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:51.987467image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:53.375458image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:54.690576image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:56.074781image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:57.371877image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:58.845415image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:00.187637image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:01.501032image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:03.000152image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:04.361281image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:05.815289image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:07.146508image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:08.659475image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:10.058730image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:11.447278image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:52.161004image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:53.460795image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:54.881649image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:56.156765image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:57.456651image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:58.930320image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:00.273407image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:01.582217image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:03.084175image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:04.448048image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:05.901564image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:07.227378image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:08.747240image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:10.142053image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:11.534611image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:52.245777image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:53.546259image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:54.965425image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:56.239544image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:57.545413image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:59.015092image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:00.360077image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:01.662004image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:03.173935image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:04.534141image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:05.991784image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:07.308317image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:08.832366image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:10.223633image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:11.622700image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:52.331547image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:53.632029image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:55.052333image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:56.323566image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:57.632181image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:59.104853image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:00.449072image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:01.745406image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:03.264863image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:04.626180image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:06.087868image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:07.617377image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:08.917139image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:10.307964image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:11.715261image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:52.418216image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:53.719304image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:55.141095image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:56.412328image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:57.726097image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:59.196731image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:00.537835image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:01.831353image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:03.359611image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:04.723060image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:06.186605image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:07.706140image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:09.005030image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:10.393029image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:11.803661image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:52.501993image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:53.801085image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:55.222877image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:56.493522image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:57.811090image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:59.283964image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:00.620176image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:01.912137image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:03.448374image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:04.811205image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:06.278359image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:07.786162image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:09.090801image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:10.473873image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:11.889294image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:52.586766image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:53.884861image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:55.306787image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:56.572311image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:57.897002image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:59.373930image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:00.706072image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:02.001898image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:03.537136image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:04.909083image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:06.369258image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:07.868522image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:09.178566image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:10.557660image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:11.981567image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:52.674113image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:53.972782image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:55.392688image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:56.656087image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:57.983771image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:51:59.466011image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:00.795832image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:02.283335image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:03.628034image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:05.001904image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:06.457023image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:07.952298image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:09.300338image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-20T16:52:10.642055image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Missing values

2024-07-20T16:52:12.166788image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-20T16:52:12.438866image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

IDYear_BirthEducationMarital_StatusIncomeKidhomeTeenhomeDt_CustomerRecencyMntWinesMntFruitsMntMeatProductsMntFishProductsMntSweetProductsMntGoldProdsNumDealsPurchasesNumWebPurchasesNumCatalogPurchasesNumStorePurchasesNumWebVisitsMonthAcceptedCmp3AcceptedCmp4AcceptedCmp5AcceptedCmp1AcceptedCmp2ComplainZ_CostContactZ_RevenueResponse
055241957GraduationSingle58138.0002012-09-04586358854617288883810470000003111
121741954GraduationSingle46344.0112014-03-08381116216211250000003110
241411965GraduationTogether71613.0002013-08-21264264912711121421821040000003110
361821984GraduationTogether26646.0102014-02-1026114201035220460000003110
453241981PhDMarried58293.0102014-01-199417343118462715553650000003110
574461967MasterTogether62513.0012013-09-09165204298042142641060000003110
69651971GraduationDivorced55635.0012012-11-133423565164504927473760000003110
761771985PhDMarried33454.0102013-05-08327610563123240480000003110
848551974PhDTogether30351.0102013-06-061914024332130290000003111
958991950PhDTogether5648.0112014-03-1368280611131100201000003110
IDYear_BirthEducationMarital_StatusIncomeKidhomeTeenhomeDt_CustomerRecencyMntWinesMntFruitsMntMeatProductsMntFishProductsMntSweetProductsMntGoldProdsNumDealsPurchasesNumWebPurchasesNumCatalogPurchasesNumStorePurchasesNumWebVisitsMonthAcceptedCmp3AcceptedCmp4AcceptedCmp5AcceptedCmp1AcceptedCmp2ComplainZ_CostContactZ_RevenueResponse
223070041984GraduationSingle11012.0102013-03-1682243267123331291000003110
223198171970MasterSingle44802.0002012-08-2171853101431310202941280000003110
223280801986GraduationSingle26816.0002012-08-1750516343100340000003110
223394321977GraduationTogether666666.0102013-06-0223914188112431360000003110
223483721974GraduationMarried34421.0102013-07-0181337629110270000003110
2235108701967GraduationMarried61223.0012013-06-13467094318242118247293450000003110
223640011946PhDTogether64014.0212014-06-1056406030008782570001003110
223772701981GraduationDivorced56981.0002014-01-2591908482173212241231360100003110
223882351956MasterTogether69245.0012014-01-248428302148030612651030000003110
223994051954PhDMarried52869.0112012-10-1540843612121331470000003111